Google Profiler
Author: Ronald Fung
Creation Date: 13 June 2023
Next Modified Date: 13 June 2024
A. Introduction
Understanding the performance of production systems is notoriously difficult. Attempting to measure performance in test environments usually fails to replicate the pressures on a production system. Microbenchmarking parts of your application is sometimes feasible, but it also typically fails to replicate the workload and behavior of a production system.
Continuous profiling of production systems is an effective way to discover where resources like CPU cycles and memory are consumed as a service operates in its working environment. But profiling adds an additional load on the production system: in order to be an acceptable way to discover patterns of resource consumption, the additional load of profiling must be small.
Cloud Profiler is a statistical, low-overhead profiler that continuously gathers CPU usage and memory-allocation information from your production applications. It attributes that information to the source code that generated it, helping you identify the parts of your application that are consuming the most resources, and otherwise illuminating your applications performance characteristics.
B. How is it used at Seagen
As a biopharma research company, Seagen can benefit from using Google Cloud Profiler for their application profiling needs. Here are some ways Seagen can use Google Cloud Profiler:
Migrating from Microsoft Azure: Seagen can migrate their existing application profiling configurations from Microsoft Azure to Google Cloud Profiler by exporting the configurations from Azure and importing them into Google Cloud Profiler. This can be done using the Google Cloud Profiler API or other migration tools.
Profiling performance data: Seagen can profile performance data from their applications, virtual machines, and other resources running on Google Cloud Platform. They can use Google Cloud Profiler to collect and analyze profiles, and can use Google Cloud Trace to trace and analyze performance data.
Monitoring and alerting: Seagen can monitor their performance and receive alerts in real-time using Google Cloud Profiler. They can set up custom alerts based on specific performance metrics or resource types, and can receive notifications via email, SMS, or other channels.
Managing access and permissions: Seagen can manage access and permissions to their performance data using Google Cloud IAM. They can define roles and permissions for different users and groups, and can control access to performance data based on resource type, location, or other attributes.
Integrating with other Google Cloud services: Seagen can integrate their performance data with other Google Cloud services, such as Google Cloud Pub/Sub, Google Cloud Storage, and Google BigQuery. They can build real-time data pipelines and analytics workflows using these services, and can store and analyze performance data at scale.
Overall, by using Google Cloud Profiler, Seagen can benefit from a powerful and scalable solution for their application profiling needs. With its support for migrating existing application profiling configurations, profiling performance data, monitoring and alerting, managing access and permissions, and integrating with other Google Cloud services, Google Cloud Profiler provides a range of options that can meet the needs of a variety of workloads and use cases.
C. Features
Google Cloud Profiler offers a range of features for application profiling needs. Here are some of the key features of Google Cloud Profiler:
Profiling collection: Google Cloud Profiler can collect profiling data from a wide range of resources, including applications, virtual machines, and other resources running on Google Cloud Platform. It can collect profiles at regular intervals or triggered by specific events or conditions, such as high CPU usage or memory usage.
Profiling analysis: Google Cloud Profiler provides profiling analysis tools, such as the Stackdriver Profiler Console, that allow users to visualize and understand the performance behavior of their applications or processes. Users can drill down into specific function calls and resource utilization metrics to identify performance bottlenecks and areas for improvement.
Profiling monitoring and alerting: Google Cloud Profiler provides monitoring and alerting capabilities that allow users to set up custom alerts based on specific performance metrics or resource types, and to receive notifications via email, SMS, or other channels. Users can monitor their performance in real-time and take action to resolve issues as they arise.
Profiling access and permissions: Google Cloud Profiler provides access and permissions management features that allow users to define roles and permissions for different users and groups, and to control access to profiling data based on resource type, location, or other attributes. This ensures that only authorized users have access to sensitive profiling data.
Integration with other Google Cloud services: Google Cloud Profiler integrates with other Google Cloud services, such as Google Cloud Trace and Google Cloud Monitoring, to provide a comprehensive application performance monitoring solution. Users can combine profiling data with other performance data to gain a deeper understanding of their applications’ behavior and to optimize resource utilization.
Support for multiple programming languages and platforms: Google Cloud Profiler supports a wide range of programming languages and platforms, including Java, Go, Python, Node.js, Ruby, and .NET, making it a versatile solution that can be used across different workloads and use cases.
Overall, with its support for profiling data collection, analysis, monitoring, and alerting, as well as integration with other Google Cloud services and support for multiple programming languages and platforms, Google Cloud Profiler is a powerful and reliable solution for application profiling needs.
D. Where Implemented
E. How it is tested
Testing Google Cloud Profiler involves ensuring that the profiling infrastructure is properly configured and optimized for performance, reliability, and security. Here are some steps to test Google Cloud Profiler:
Create a test environment: Create a test environment that mimics the production environment as closely as possible, including the applications, virtual machines, and other resources that generate performance data. Ensure that the profiling infrastructure is properly configured and that the security policies are in place.
Deploy the profiling infrastructure: Deploy the profiling infrastructure on Google Cloud Platform. Ensure that the infrastructure is properly configured and that it can communicate with other resources, such as applications or APIs.
Test profiling collection: Test the profiling collection by generating test performance data using performance generation tools, such as the Stackdriver Profiler API or other performance generation tools. Ensure that the performance data is being collected properly and that there are no errors or missing data.
Test profiling analysis: Test the profiling analysis by using profiling analysis tools, such as the Stackdriver Profiler Console or other profiling analysis tools. Ensure that the profiles are searchable and that there are no errors or timeouts.
Test profiling monitoring and alerting: Test the profiling monitoring and alerting by setting up custom alerts based on specific performance metrics or resource types, and by receiving notifications via email, SMS, or other channels. Ensure that the alerts are triggered properly and that there are no false positives or false negatives.
Test profiling access and permissions: Test the profiling access and permissions by using Google Cloud IAM to define roles and permissions for different users and groups, and by controlling access to profiles based on resource type, location, or other attributes. Ensure that the access control policies are working as expected and that there are no unauthorized access or data breaches.
Test profiling integration: Test the profiling integration with other Google Cloud services or third-party tools by using profiling integration testing tools, such as Google Cloud Pub/Sub or Microsoft Azure Event Grid. Ensure that the profiles are integrated properly and that there are no integration issues or errors.
Overall, by thoroughly testing Google Cloud Profiler, users can ensure that their profiling infrastructure is properly configured and optimized for performance, reliability, and security. Additionally, users can reach out to Google Cloud support for help with any technical challenges they may encounter.
F. 2023 Roadmap
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G. 2024 Roadmap
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H. Known Issues
While Google Cloud Profiler is a reliable and widely used solution for application profiling needs, there are some known issues that users may encounter. Here are some of the known issues for Google Cloud Profiler:
Profiling data accuracy issues: Users may encounter issues with profiling data accuracy, such as missing or incomplete profiles, especially for workloads that generate a high volume of profiles. This issue can often be resolved by using the appropriate profiling configuration settings, such as profile sampling rates or profile exclusions, and by monitoring the profiling data accuracy.
Profiling performance issues: Users may encounter issues with profiling performance, such as slow profiles or high latency, especially for workloads that require real-time profiling or low latency profiling. This issue can often be resolved by using the appropriate profiling configuration settings, such as profile aggregation or profile compression, and by optimizing the profiling performance.
Alerting issues: Users may encounter alerting issues, such as false positives or false negatives, especially for workloads that require high accuracy or low latency alerts. This issue can often be resolved by using the appropriate alerting policies, such as performance threshold values or alert conditions, and by testing the alerting policies in a test environment.
Security issues: Users may encounter security issues, such as unauthorized access or data breaches, especially for workloads that require high security. This issue can often be resolved by using the appropriate security policies and access controls, such as firewall rules and IAM roles.
Integration issues: Users may encounter integration issues with other cloud services or third-party tools, such as data pipelines or analytics platforms. This issue can often be resolved by using industry-standard protocols and APIs to enable interoperability between different cloud services and tools.
Overall, while these issues may impact some users, Google Cloud Profiler remains a powerful and reliable solution that is widely used by businesses and organizations around the world. By monitoring their performance and security alerts and profiles, reviewing their profiling configuration and policies, and using best practices and industry standards, users can ensure that their profiling infrastructure running on Google Cloud Profiler is optimized for performance, reliability, and security. Additionally, users can reach out to Google Cloud support for help with any known issues or other technical challenges they may encounter.
[x] Reviewed by Enterprise Architecture
[x] Reviewed by Application Development
[x] Reviewed by Data Architecture